This application generally relates to techniques that may be used in connection with data compression.
Data compression may be characterized as the process of encoding source information using an encoding scheme into a compressed form having fewer bits than the original or source information. Different encoding schemes may be used in connection with data compression. One class of data compression techniques is generally known as lossless data compression techniques allowing the exact original information to be reconstructed from the compressed form. Such techniques of the first class may be used when it is important that the original and decompressed forms remain bitwise identical. Another class of data compression techniques is generally known as lossy data compression techniques in which there is some acceptable loss or difference between the original and decompressed forms. Lossy compression techniques may utilize quality information indicating which portions of the source information are more or less important in comparison to other portions of the source information. The lossy compression techniques may disregard the less important information while still retaining the other more important information.
In a compression system, an encoder may be used in producing a compressed form of the original or source information. The compressed form may subsequently be processed by a decoder to form a decompressed reconstruction of the original information. In connection with performing compression, one technique that may be used is the Burrows-Wheeler transform (BWT, also referred to as block-sorting compression), as developed by Michael Burrows and David Wheeler. For data compression, the BWT algorithm may be used in combination with one or more other techniques to compress input data.